Literature DB >> 29761595

Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation.

Ying Liu1, Yuwei Zhang1,2, Runfen Cheng3, Shichang Liu1, Fangyuan Qu1, Xiaoyu Yin1, Qin Wang1, Bohan Xiao1, Zhaoxiang Ye1.   

Abstract

BACKGROUND: The role of apparent diffusion coefficient (ADC)-based radiomics features in evaluating histopathological grade of cervical cancer is unresolved.
PURPOSE: To determine if there is a difference between radiomics features derived from center-slice 2D versus whole-tumor volumetric 3D for ADC measurements in patients with cervical cancer regarding tumor histopathological grade, and systematically assess the impact of the b value on radiomics analysis in ADC quantifications. STUDY TYPE: Prospective.
SUBJECTS: In all, 160 patients with histopathologically confirmed squamous cell carcinoma of uterine cervix. FIELD STRENGTH/SEQUENCE: Conventional and diffusion-weighted MR images (b values = 0, 800, 1000 s/mm2 ) were acquired on a 3.0T MR scanner. ASSESSMENT: Regions of interest (ROIs) were drawn manually along the margin of tumor on each slice, and then the center slice of the tumor was selected with naked eyes in the course of whole-tumor segmentation. A total of 624 radiomics features were derived from T2 -weighted images and ADC maps. We randomly selected 50 cases and did the reproducibility analysis. STATISTICAL TESTS: Parameters were compared using Wilcoxon signed rank test, Bland-Altman analysis, t-test, and least absolute shrinkage and selection operator (LASSO) regression with crossvalidation.
RESULTS: In all, 95 radiomics features were insensitive to ROI variation among T2 images, ADC map of b800, and ADC map of b1000 (P > 0.0002). There was a significant statistical difference between the performances of 2D center-slice and 3D whole-tumor radiomics models in both ADC feature sets of b800 and b1000 (P < 0.0001, P < 0.0001). Compared with ADC features of b800 (0.3758 ± 0.0118), the model of b1000 ADC features appeared to be slightly lower in overall misclassification error (0.3642 ± 0.0162) (P = 0.0076). DATA
CONCLUSION: Several radiomics features extracted from T2 images and ADC maps were highly reproducible. Whole-tumor volumetric 3D radiomics analysis had a better performance than using the 2D center-slice of tumor in stratifying the histological grade of cervical cancer. A b value of 1000 s/mm2 is suggested as the optimal parameter in pelvic DWI scans. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:280-290.
© 2018 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  apparent diffusion coefficient; cervical cancer; diffusion-weighted magnetic resonance imaging; histopathological grade; radiomics

Mesh:

Year:  2018        PMID: 29761595     DOI: 10.1002/jmri.26192

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  26 in total

1.  Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.

Authors:  He Zhang; Yunfei Mao; Xiaojun Chen; Guoqing Wu; Xuefen Liu; Peng Zhang; Yu Bai; Pengcong Lu; Weigen Yao; Yuanyuan Wang; Jinhua Yu; Guofu Zhang
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

2.  Lack of evidence and criteria to evaluate artificial intelligence and radiomics tools to be implemented in clinical settings.

Authors:  Qian Zhou; Yi-Heng Cao; Zhi-Hang Chen
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3.  An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer.

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Review 4.  Challenges in ensuring the generalizability of image quantitation methods for MRI.

Authors:  Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza
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5.  The Value of Whole-Tumor Texture Analysis of ADC in Predicting the Early Recurrence of Locally Advanced Cervical Squamous Cell Cancer Treated With Concurrent Chemoradiotherapy.

Authors:  Xiaomiao Zhang; Qi Zhang; Lizhi Xie; Jusheng An; Sicong Wang; Xiaoduo Yu; Xinming Zhao
Journal:  Front Oncol       Date:  2022-05-20       Impact factor: 5.738

6.  Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.

Authors:  Yu-Chun Lin; Chia-Hung Lin; Hsin-Ying Lu; Hsin-Ju Chiang; Ho-Kai Wang; Yu-Ting Huang; Shu-Hang Ng; Ji-Hong Hong; Tzu-Chen Yen; Chyong-Huey Lai; Gigin Lin
Journal:  Eur Radiol       Date:  2019-11-11       Impact factor: 5.315

7.  CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma.

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Journal:  Front Oncol       Date:  2021-05-24       Impact factor: 6.244

Review 8.  Implications of the new FIGO staging and the role of imaging in cervical cancer.

Authors:  Aki Kido; Yuji Nakamoto
Journal:  Br J Radiol       Date:  2021-05-14       Impact factor: 3.629

Review 9.  Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review.

Authors:  Seung Hak Lee; Hyunjin Park; Eun Sook Ko
Journal:  Korean J Radiol       Date:  2020-07       Impact factor: 3.500

10.  Knockdown of BRCC3 exerts an anti‑tumor effect on cervical cancer in vitro.

Authors:  Feifang Zhang; Qun Zhou
Journal:  Mol Med Rep       Date:  2018-09-26       Impact factor: 2.952

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